watch_trained_agent.py

from unityagents import UnityEnvironment
import numpy as np
from collections import deque
import matplotlib.pyplot as plt
import torch

from ddpg_agent import Agent

# 1. Create environment
env = UnityEnvironment(
    # file_name="simulator/Reacher_Single/Reacher.x86_64")
    file_name="simulator/Reacher_Multi/Reacher.x86_64")
brain_name = env.brain_names[0]
brain = env.brains[brain_name]
env_info = env.reset(train_mode=True)[brain_name]
num_agents = len(env_info.agents)
action_size = brain.vector_action_space_size
states = env_info.vector_observations
state_size = states.shape[1]
print('Number of agents:', num_agents)

# 2. Create agent
agent = Agent(state_size, action_size, random_seed=14)

# 3. Load the previously learned weights from file
agent.actor_local.load_state_dict(torch.load('checkpoint_actor.pth'))
agent.critic_local.load_state_dict(torch.load('checkpoint_critic.pth'))

# 4. Apply trained agent to solve a full episode
env_info = env.reset(train_mode=False)[brain_name]     # reset the environment    
states = env_info.vector_observations                  # get the current state (for each agent)
scores = np.zeros(num_agents)                          # initialize the score (for each agent)
while True:
    actions = agent.act(states)                        # select an action (for each agent)
    env_info = env.step(actions)[brain_name]           # send all actions to tne environment
    next_states = env_info.vector_observations         # get next state (for each agent)
    rewards = env_info.rewards                         # get reward (for each agent)
    dones = env_info.local_done                        # see if episode finished
    scores += env_info.rewards                         # update the score (for each agent)
    states = next_states                               # roll over states to next time step
    if np.any(dones):                                  # exit loop if episode finished
        break
print('Total score (averaged over agents) this episode: {}'.format(np.mean(scores)))

env.close()